Deep Pre-Trained Model Menggunakan Arsitektur Densenet Untuk Identifikasi Penyakit Daun Padi

Faizin, Arif; Arsanto, Arief Tri; Lutfi, Moch.; Musa, Abdur Rochim

doi:10.36040/jati.v6i2.5475

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Discussion about deep learning using DenseNet to detect diseases in rice leaves. The title of his research "Depp Pre-Trained Model Using DenseNet Architecture for Identification of Rice Leaf Diseases" (Faizin et al, 2022) DenseNet, is an algorithm of the Convolutional Neural Network which has similarities to the ResNet architecture. The additive method is an additional process used by Resnet to retrieve the previous output in the input process at the layer after the ResNet process.…”

Section: Literature Reviewmentioning

confidence: 99%

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Saputra

Hindarto

Santoso

2023

SinkrOn

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Rice is a plant that can grow in the tropics. This plant can produce food that can meet the needs of the people of a country. This plant can grow well if it is cared for properly. If the planting has used good care, such as providing adequate water, adding good fertilizer, it can be ascertained that it will produce a lot of rice fruit after harvesting. This often causes concern if rice growers have given good care but often produce less rice fruit because rice plants are attacked by various diseases. This is what makes the problem, that rice plants are attacked by diseases. Before spraying diseases or pests, farmers should have an understanding of diseases in rice. This makes farmers not wrong in choosing drugs for farmers' rice. It is very vulnerable if farmers do not know about the rice disease. Therefore, it is necessary to observe what types of rice diseases attack rice plants. Observations are not enough just to take pictures with a camera. But it is necessary to carry out further analysis of rice diseases. The presence of information technology is now able to recognize any type. One of the machine learning technologies is able to detect rice diseases. One of these branches of machine learning is deep learning. By using a dataset that focuses on rice disease, the model generated from deep learning training is able to detect rice disease. The purpose of this research is to predict disease in rice leaves using deep learning, namely DenseNet. Training using DenseNet, namely DenseNet121, DenseNet169 and DenseNet201. Accuracy using DenseNet121 reached 91.67%, DenseNet169 reached 90%, and DenseNet201 reached 88.33%. The model training time takes 24 seconds.

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Section: Literature Reviewmentioning

confidence: 99%

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Saputra

Hindarto

Santoso

2023

SinkrOn

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“…Daun padi mengalami penyakit berbagai gejala, serta perubahan warna bahkan pada daun yang permukaannya terdapat becak. penyakit daun yang paling sering menyerang tanaman padi yaitu Blast, brown spot, leaf smut, dan penyakit lainnya [6].…”

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Augmentasi Data Pada Implementasi Convolutional Neural Network Arsitektur Efficientnet-B3 Untuk Klasifikasi Penyakit Daun Padi

Putri Ayuni,

Jasril,

Irsyad

et al. 2023

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Padi adalah salah satu jenis biji-bijian dengan urutan ketiga sebagai bahan pokok makanan setelah gandum dan jagung. Jenis penyakit yang menyerang daun tanaman terdiri atas blast, brownspot, leaf smut. Pada penelitian ini metode Convolutional Neural Network dengan Arsitektur Efficientnet-B3 digunakan untuk mengklasifikasikan penyakit daun pada tanaman padi. Tujuan penelitian ini membandingkan tingkat akurasi menggunakan data tanpa augmentasi (asli) dan data yang telah di augmentasi. Augmentasi data yang digunakan brightness, rotation, dan vertical_flip. Selain itu dilakukan juga pengujian menggunakan optimizer yang berbeda yaitu optimizer RMSprop dan optimizer SGD (Stochastic Gradient Descent). Pengujian dilakukan dengan tiga model perbandingan data yaitu 90:10, 80;20 dan 70:30. Hasil pengujian memperlihatkan akurasi tertinggi menggunakan data asli pada rasio 70:30 yaitu sebesar 92.39% dengan optimizer RMSprop. Sedangkan untuk akurasi tertinggi menggunakan data augmentasi terdapat pada rasio 90:10 yaitu sebesar 98.91% dengan optimizer RMSprop.

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Deep Pre-Trained Model Menggunakan Arsitektur Densenet Untuk Identifikasi Penyakit Daun Padi

Cited by 2 publications

References 12 publications

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Augmentasi Data Pada Implementasi Convolutional Neural Network Arsitektur Efficientnet-B3 Untuk Klasifikasi Penyakit Daun Padi

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